Magnetic resonance imaging (MRI) is an extremely powerful medical imaging modality. However, this tool is hindered by lengthy acquisitions required for each scan. A fully-sampled high-resolution volumetric scan can take on the order of 5 minutes. Thus, image quality and image resolutions are typically compromised for clinical feasibility and for minimizing artifacts. Moreover, lengthy acquisitions impacts patient comfort and limits the modality to patients who can remain cooperative and still during the extent of the imaging exam.
The combined advancement of imaging receiver hardware with high channel count and model-based reconstruction has enabled the ability to shorten the data acquisition through data subsampling while maintaining high spatial resolutions and high image quality. The set of algorithms, referred as parallel imaging, enables routine clinical scans to be accelerated up to 8-folds: a 5 minutes scan becomes 40 seconds. In parallel imaging, localized sensitivity profiles of each element in a multi-channel coil receiver array are modeled and leveraged to reconstruct high-quality medical images. Prior information about the images can also be included to enable higher subsampling factors—over 2 times the subsampling factor over parallel imaging alone. For example, sparsity in the Wavelet transform domain or sparsity in the finite differences can be leveraged in a compressed-sensing-based image reconstruction. Additionally, these image priors and iterative reconstruction process can be learned via deep learning approaches.
The assortment of model-based techniques to enable high scan reduction through data subsampling all rely on the ability to accurately estimate the imaging model specific for each scan. This imaging model characterizes the process of transforming the desired image to the measured data. The model typically includes the sensitivity maps of each coil receiver, the Fourier transform operator, and data sampling. For optimal performance, sensitivity maps must be characterized for each scan. A separate calibration scan can be performed to measure this information but at the cost of an increase in exam time. Moreover, patient motion may result in mis-registration between calibration and the actual imaging study. The acquisition of the calibration information can be included intrinsically in the scan itself. However, for highly subsampled acquisition, the requirement to include a calibration region in the scan becomes more costly in proportion to the total scan time.
Not only can acquiring calibration data be challenging, accurate estimation of the sensitivity profile information can be computationally expensive especially for multi-dimensional acquisitions (3+ dimensions) and for setups with high coil count (32+ channels) which can take on the order of a few minutes. With the application of deep learning that has enabled massive gains in computational speed (to be on the order of milliseconds) for image reconstruction, the lengthy time needed to estimate the sensitivity profiles becomes a major bottleneck.